1. Technical Field
The present invention relates to image segmentation, and more particularly, to a system and method for segmenting a structure of interest by using an interpolation of a separating surface in an area of attachment to a structure having similar properties.
2. Discussion of the Related Art
Current computer-aided detection and diagnosis (CAD) techniques have been used for the early detection and screening of cancer in areas of the body such as the colon and lungs. For example, CAD techniques such as computed tomography (CT) colonography can locate and identify polyps on the colon wall by using geometric features of the colon surface and/or volumetric properties near the surface to assist with detection. Recently, such techniques have been enhanced to segment polyps to provide the entire voxel set of the polyp. This data can then be used to quantify certain characteristics of the polyp. CAD techniques such as virtual endoscopy based on two-dimensional (2D) and three-dimensional (3D) analysis of image data acquired during diagnostic CT scans can be used locate and identify lung nodules or aneurisms by using geometric features and/or volumetric properties of the lung and its vessel trees. Similar to CT colonoscopy, virtual bronchoscopy and lung nodule detection in chest CT scans includes advanced segmentation methods to provide a medical practitioner with information regarding detected thoracic and lung nodules. This data could then be used to quantify certain characteristics of the nodules and aid in the diagnosis of diseases associated therewith.
In the above-mentioned and other areas of CAD, the surface or boundary between, for example, a segmented polyp and a colon or bronchi lumen or between a lung nodule and the air inside the lungs is relatively easy to determine due to the large intensity discrepancy between these regions. However, the boundaries between polyp tissues of similar intensity are not very obvious. For example, a polyp 110 is always connected to another object having a similar intensity such as a colon fold 120 or colon wall 130 as shown in FIG. 1. Thus, in order to determine the surface or boundary between the polyp 110 and the colon fold 120 or wall 130 special image-processing techniques need to be applied to successfully locate and separate them.
In one image processing technique for polyp segmentation, a hysteresis thresholding is performed that uses some colonic volumetric features. This technique first locates voxels with low curvedness values and high shape indexes and then clusters them to segment the polyp. This technique, however, does not sufficiently identify small polyps and flat polyps since there is an insufficient number of voxels with low curvedness values and high shape indexes for clustering. In another image processing technique, Canny edge detectors are used to locate the polyp-lumen boundary and Radon transformation is used to identify round shaped polyps. This technique, however, does not robustly segment all polyp types.
In yet another image processing technique, a combination of knowledge-guided intensity adjustments, fuzzy c-mean clustering and deformable models is employed for locating the polyp boundary. However, this technique does not robustly segment abnormal growths from healthy tissue. Accordingly, there is a need for a segmentation technique that is capable of robustly segmenting abnormal growths or similar structures from healthy tissue or other nearby structures so that the size and structure of the abnormal growth can be monitored over time and presented to a medical practitioner for analysis.